# coding=utf-8
# 决策树
from math import log
import operator



#计算给定数据集的香农熵
from com.csu.learnPython.treePlotter import *


def calcShannonEnt(dataSet):
    numEntries = len(dataSet)
    labelCounts = {}
    for featVec in dataSet:
        currentLabel = featVec[-1]
        if currentLabel not in labelCounts.keys():
            labelCounts[currentLabel] = 0
            labelCounts[currentLabel] += 1

    shannonEnt = 0.0
    for key in labelCounts.keys():
        prob = float(labelCounts[key])/numEntries
        shannonEnt -= prob * log(prob, 2)

    return shannonEnt

def createDataSet():
    dataSet = [[1,1,'yes'],
               [1,1,'yes'],
               [1,0,'no'],
               [0,1,'no'],
               [0,1,'no']]
    labels = ['no surfacing', 'flippers']
    return dataSet,labels


#按照给定特征划分数据集
def splitDataSet(dataSet, axis, value): #待划分数据集，特征，特征的返回值
    retDataSet = []
    for featVec in dataSet:
        if featVec[axis] == value:
            reducedFeatVec = featVec[:axis]
            reducedFeatVec.extend(featVec[axis+1:])
            retDataSet.append(reducedFeatVec)

    return retDataSet

#选择最好的数据集划分方式
def chooseBestFeatureToSplit(dataSet):
    numFeatures = len(dataSet[0]) - 1
    baseEntropy = calcShannonEnt(dataSet)   #原始的熵
    bestInfoGain = 0.0; bestFeature = -1

    for i in range(numFeatures):
        featList = [example[i] for example in dataSet]  #获取第i个特征的所有可能取值
        uniqueVals = set(featList)
        newEntropy = 0.0
        for value in uniqueVals:
            subDataSet = splitDataSet(dataSet, i, value)
            prob = len(subDataSet)/float(len(dataSet))
            newEntropy += prob * calcShannonEnt(subDataSet)

        infoGain = baseEntropy = newEntropy
        if infoGain > bestInfoGain:
            bestInfoGain = infoGain
            bestFeature = i

    return bestFeature

#多数表决
def majorityCnt(classList):
    classCount = {}
    for vote in classList:
        if vote not in classCount.keys():
            classCount[vote] = 0
        classCount[vote] += 1

    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

#创建决策树(使用递归)
def createTree(dataSet, labels):
    classList = [example[-1] for example in dataSet]    #获取所有的类别标签
    if classList.count(classList[0]) == len(classList): #如果类别都相同，则停止继续划分
        return classList[0]

    if len(dataSet[0]) == 1:    #如果只剩了类别，说明已经遍历玩所有特征了，返回出现次数最多的类别
        return majorityCnt(classList)

    bestFeat = chooseBestFeatureToSplit(dataSet)
    bestFeatLabel = labels[bestFeat]
    myTree = {bestFeatLabel:{}}
    del(labels[bestFeat])       #删除labels[bestFeat]

    featValues = [example[bestFeat] for example in dataSet]
    uniqueVals = set(featValues)
    for value in uniqueVals:
        subLabels = labels[:]
        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels)

    return myTree

##使用决策树的分类函数
def classify(inputTree, featLabels, testVec):
    firstStr = list(inputTree.keys())[0]
    secondDict = inputTree[firstStr]
    featIndex = featLabels.index(firstStr)
    key = testVec[featIndex]
    valueOfFeat = secondDict[key]
    if isinstance(valueOfFeat, dict):
        classLabel = classify(valueOfFeat, featLabels, testVec)
    else:
        classLabel = valueOfFeat
    return classLabel

#使用pickle模块存储决策树
def storeTree(inputTree, filename):
    import pickle
    fw = open(filename, 'wb')
    pickle.dump(inputTree, fw)
    fw.close()

def grabTree(filename):
    import pickle
    fr = open(filename,'rb')
    return pickle.load(fr)


def main():
    myDat, labels = createDataSet()
    print(labels)
    myTree = retriveTree(0)
    print(myTree)
    print(classify(myTree, labels, [1,0]))

    storeTree(myTree, "classifyTree.txt")
    print(grabTree("classifyTree.txt"))

    # myDat[0][-1] = 'maybe'
    # print(calcShannonEnt(myDat))
    # print(splitDataSet(myDat, 0, 1))
    # print(splitDataSet(myDat, 0, 0))
    # print(chooseBestFeatureToSplit(myDat))


if __name__ == '__main__':
    main()
